Data Wrangling
We re-coded and re-leveled variables (Treatment and
Group), cleans column names, and renames specific columns
(x11_oh_thc to thcoh, thc_v to
thcv, thccooh_gluc to
thc_cooh_gluc, and thccooh to
thc_cooh) in all 3 tables. Using janitor
package to organized column names.
**Suggestion: add a new column recording whether someone should be
determined to have recent THC use (THC group, within 3 hr of smoking) or
not(all placebo group, or THC group outside the 3hr window)
OF <- OF |>
mutate(Treatment = fct_recode(Treatment,
"5.9% THC (low dose)" = "5.90%",
"13.4% THC (high dose)" = "13.40%"),
Treatment = fct_relevel(Treatment, "Placebo", "5.9% THC (low dose)"),
Group = fct_recode(Group,
"Occasional user" = "Not experienced user",
"Frequent user" = "Experienced user" )) |>
janitor::clean_names() |>
rename(thcoh = x11_oh_thc,
thcv = thc_v)
WB <- WB |>
mutate(Treatment = fct_recode(Treatment,
"5.9% THC (low dose)" = "5.90%",
"13.4% THC (high dose)" = "13.40%"),
Treatment = fct_relevel(Treatment, "Placebo", "5.9% THC (low dose)")) |>
janitor::clean_names() |>
rename(fluid = fluid_type,
thcoh = x11_oh_thc,
thccooh = thc_cooh,
thccooh_gluc = thc_cooh_gluc,
thcv = thc_v)
BR <- BR |>
mutate(Treatment = fct_recode(Treatment,
"5.9% THC (low dose)" = "5.90%",
"13.4% THC (high dose)" = "13.40%"),
Treatment = fct_relevel(Treatment, "Placebo", "5.9% THC (low dose)"),
Group = fct_recode(Group,
"Occasional user" = "Not experienced user",
"Frequent user" = "Experienced user" )) |>
janitor::clean_names() |>
rename(thc = thc_pg_pad)
compounds_WB <- as.list(colnames(Filter(function(x) !all(is.na(x)), WB[6:13])))
compounds_BR <- as.list(colnames(Filter(function(x) !all(is.na(x)), BR[6])))
compounds_OF <- as.list(colnames(Filter(function(x) !all(is.na(x)), OF[6:12])))
Created 3 tables based on specific minutes and labeled accordingly,
covering pre-smoking and subsequent post-smoking time periods for blood,
breath, and oral fluid data.
timepoints_WB <- tibble(
start = c(-400, 0, 30, 70, 100, 180, 210, 240, 270, 300),
stop = c(
0,
30,
70,
100,
180,
210,
240,
270,
300,
max(WB$time_from_start, na.rm = TRUE)
),
timepoint = c(
"pre-smoking",
"0-30 min",
"31-70 min",
"71-100 min",
"101-180 min",
"181-210 min",
"211-240 min",
"241-270 min",
"271-300 min",
"301+ min"
)
)
timepoints_BR <- tibble(
start = c(-400, 0, 40, 90, 180, 210, 240, 270),
stop = c(
0,
40,
90,
180,
210,
240,
270,
max(BR$time_from_start, na.rm = TRUE)
),
timepoint = c(
"pre-smoking",
"0-40 min",
"41-90 min",
"91-180 min",
"181-210 min",
"211-240 min",
"241-270 min",
"271+ min"
)
)
timepoints_OF <- tibble(
start = c(-400, 0, 30, 90, 180, 210, 240, 270),
stop = c(0, 30, 90, 180, 210, 240, 270,
max(OF$time_from_start, na.rm = TRUE)),
timepoint = c(
"pre-smoking",
"0-30 min",
"31-90 min",
"91-180 min",
"181-210 min",
"211-240 min",
"241-270 min",
"271+ min"
)
)
assign_timepoint <- function(x, timepoints) {
if (!is.na(x)) {
timepoints$timepoint[x > timepoints$start & x <= timepoints$stop]
} else{
NA
}
}
We created a new column, timepoint_use,
in each table by mapping the
time_from_start values to specific
timepoints defined in separate reference data frames
(timepoints_WB,
timepoints_OF,
timepoints_BR). Finally, re-leveled the
timepoint_use factor variable to align
with the order specified in the reference data frames. This ensures
consistent and meaningful timepoint labels for subsequent analyses or
visualizations in the study.
WB <- WB |>
mutate(timepoint_use = map_chr(time_from_start,
assign_timepoint,
timepoints=timepoints_WB),
timepoint_use = fct_relevel(timepoint_use, timepoints_WB$timepoint))
OF <- OF |>
mutate(timepoint_use = map_chr(time_from_start,
assign_timepoint,
timepoints=timepoints_OF),
timepoint_use = fct_relevel(timepoint_use, timepoints_OF$timepoint))
BR <- BR |>
mutate(timepoint_use = map_chr(time_from_start,
assign_timepoint,
timepoints=timepoints_BR),
timepoint_use = fct_relevel(timepoint_use, timepoints_BR$timepoint))
remove duplicate id
WB <- drop_dups(WB)
OF <- drop_dups(OF)
BR <- drop_dups(BR)
